HCDE faculty, students, staff, and invited guests are welcome to join the department for a dissertation defense presentation by PhD Candidate Christina Chung.

Using Personal Informatics Data in Collaboration with People with Different ExpertiseCandidate: Christina Chung

Dissertation abstract:

Many people collect and analyze data about themselves with the expectation to improve their health and wellbeing. With the prevalence of smartphones and wearable sensors, people are able to collect detailed and complex data about their everyday behavior, such as food, exercise, and sleep. This everyday behavioral data can support individual health goal, manage health conditions, and complement traditional medical examinations conducted in the clinical visits. However, people often need support to interpret this self-tracked data. Many people share their data with health experts, hoping to use this data to support more personalized diagnosis and recommendations as well as to receive emotional support from the experts. However, when attempting to use this data in collaboration, people and their health experts often struggle with making sense of the data.

My dissertation studies how to support people and health experts to collaborate using personal informatics data. My research builds an empirical understanding about individual and collaboration goals around using personal informatics data, their current practices for using this data to support collaboration, and challenges and expectations of integrating this practice into clinical workflow. These understandings help designers and researchers advance the design of personal informatics systems and theoretical understandings of patient-provider collaboration. Based on these findings, I propose design and theoretical considerations regarding interactions among people, health experts, and personal informatics data. System designers and personal informatics researchers need to consider tracking as collaborative activities and support these activities throughout the tracking process. Patient-provider collaboration might influence individual decisions to track and to review, and systems supporting this collaboration needs to consider individual and collaborative goals as well as support communication around these goals. Designers and researchers also should attend to individual privacy needs when the personal informatics data is shared and used among different healthcare context.

With these design guidelines in mind, I design and develop Foodprint, a photo-based food diary and visualization system. I also conduct field evaluations to understand use of lightweight data collection and integration to support collaboration around personal informatics data. Findings from these field deployments showed that because photo-based visualizations allow participants and health experts to easily understand eating patterns relevant to individual health goals, they could focus on individual health goals and questions, exchange knowledge to support individualized diagnosis and recommendations, and develop plans actionable and feasible to accommodate individual routine.

My dissertation also examines ways of incorporating personal informatics data in the clinic. Many clinics start to encourage patient to self-collect data that is traditionally collected by medical professionals, with the expectation to increase clinic efficiency and promote patient awareness. One such example is the self-service blood pressure kiosks. In a longitudinal, mixed-method study, I partnered with medical researchers to examine the adoption of self-service blood pressure kiosks in a family medicine clinic. Despite initial concerns raised by patients, staff, and clinicians, we found that the clinic iteratively identified and addressed emerging challenges, provided timely solutions, and improved the overall workflow integration.

My dissertation research expands our knowledge of how we should design systems and workflows to support personal informatics data use in collaborative care and to improve patient-provider communication about their goals and expected use of this data. It extends current theories and frameworks around personal informatics and collaborative work to describe interactions among people with different expertise and to discuss the potential impact to individual and collaborative practices. It also provides a foundation for future researchers to study how personal informatics data can support management of other chronic disease or preventive care as well as how people with different expertise interact, communicate, and collaborate with each other.